Sustainable Crop Yield Prediction

Abstract

Accurate forecasting of crop yields is fundamental to improving agricultural efficiency and ensuring global food availability. This research implements machine learning methodologies to estimate crop output using key agronomic and environmental indicators, such as precipitation, pesticide application, mean temperature, and carbon emissions. A web interface built with Flask enhances usability for farmers and agricultural professionals. This modern approach demonstrates improved predictive accuracy and accessibility compared to conventional statistical techniques.